{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|default_exp models.PatchTST"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PatchTST"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an unofficial PyTorch implementation of PatchTST created by Ignacio Oguiza (oguiza@timeseriesAI.co) based on:\n",
    "\n",
    "In this notebook, we are going to use a new state-of-the-art model called PatchTST (Nie et al, 2022) to create a long-term time series forecast. \n",
    "\n",
    "\n",
    "\n",
    "Here are some paper details:\n",
    "\n",
    "* Nie, Y., Nguyen, N. H., Sinthong, P., & Kalagnanam, J. (2022). **A Time Series is Worth 64 Words: Long-term Forecasting with Transformers.** arXiv preprint arXiv:2211.14730.\n",
    "* Official implementation:: https://github.com/yuqinie98/PatchTST\n",
    "\n",
    "```bash\n",
    "@article{Yuqietal-2022-PatchTST,\n",
    "  title={A Time Series is Worth 64 Words: Long-term Forecasting with Transformers},\n",
    "  author={Yuqi Nie and \n",
    "          Nam H. Nguyen and \n",
    "          Phanwadee Sinthong and \n",
    "          Jayant Kalagnanam},\n",
    "  journal={arXiv preprint arXiv:2211.14730},\n",
    "  year={2022}\n",
    "}\n",
    "```\n",
    "\n",
    "PatchTST has shown some impressive results across some of the most widely used long-term datasets for benchmarking:\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "import warnings\n",
    "from typing import Optional\n",
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "from torch import Tensor\n",
    "from tsai.models.layers import Transpose, get_act_fn, RevIN\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "class MovingAverage(nn.Module):\n",
    "    \"Moving average block to highlight the trend of time series\"\n",
    "\n",
    "    def __init__(self,\n",
    "         kernel_size:int,  # the size of the window\n",
    "         ):\n",
    "        super().__init__()\n",
    "        padding_left = (kernel_size - 1) // 2\n",
    "        padding_right = kernel_size - padding_left - 1\n",
    "        self.padding = torch.nn.ReplicationPad1d((padding_left, padding_right))\n",
    "        self.avg = nn.AvgPool1d(kernel_size=kernel_size, stride=1)\n",
    "\n",
    "    def forward(self, x:Tensor):\n",
    "        \"\"\" \n",
    "        Args:\n",
    "            x: torch.Tensor shape: [bs x seq_len x features]\n",
    "        \"\"\"\n",
    "        return self.avg(self.padding(x))\n",
    "\n",
    "\n",
    "class SeriesDecomposition(nn.Module):\n",
    "    \"Series decomposition block\"\n",
    "\n",
    "    def __init__(self,\n",
    "         kernel_size:int,  # the size of the window\n",
    "         ):\n",
    "        super().__init__()\n",
    "        self.moving_avg = MovingAverage(kernel_size)\n",
    "\n",
    "    def forward(self, x:Tensor):\n",
    "        \"\"\" Args:\n",
    "            x: torch.Tensor shape: [bs x seq_len x features]\n",
    "        \"\"\"\n",
    "        moving_mean = self.moving_avg(x)\n",
    "        residual = x - moving_mean\n",
    "        return residual, moving_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|exporti\n",
    "class _ScaledDotProductAttention(nn.Module):\n",
    "    r\"\"\"Scaled Dot-Product Attention module (Attention is all you need by Vaswani et al., 2017) with \n",
    "    optional residual attention from previous layer\n",
    "    Realformer: Transformer likes residual attention by He et al, 2020\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, d_model, n_heads, attn_dropout=0., res_attention=False):\n",
    "        super().__init__()\n",
    "        self.attn_dropout = nn.Dropout(attn_dropout)\n",
    "        self.res_attention = res_attention\n",
    "        head_dim = d_model // n_heads\n",
    "        self.scale = nn.Parameter(torch.tensor(\n",
    "            head_dim ** -0.5), requires_grad=False)\n",
    "\n",
    "    def forward(self, q:Tensor, k:Tensor, v:Tensor, prev:Optional[Tensor]=None):\n",
    "        '''\n",
    "        Input shape:\n",
    "            q               : [bs x n_heads x max_q_len x d_k] # d_k = d_model // n_heads\n",
    "            k               : [bs x n_heads x d_k x seq_len]   # d_k = d_model // n_heads\n",
    "            v               : [bs x n_heads x seq_len x d_v]   # d_v = d_model // n_heads\n",
    "            prev            : [bs x n_heads x q_len x seq_len]\n",
    "        Output shape:\n",
    "            output          :  [bs x n_heads x q_len x d_v]    # d_v = d_model // n_heads\n",
    "            attn            : [bs x n_heads x q_len x seq_len]\n",
    "            scores          : [bs x n_heads x q_len x seq_len]\n",
    "        '''\n",
    "\n",
    "        # Scaled MatMul (q, k) - similarity scores for all pairs of positions in an input sequence\n",
    "        # attn_scores : [bs x n_heads x max_q_len x q_len]\n",
    "        attn_scores = torch.matmul(q, k) * self.scale\n",
    "\n",
    "        # Add pre-softmax attention scores from the previous layer (optional)\n",
    "        if prev is not None:\n",
    "            attn_scores = attn_scores + prev\n",
    "\n",
    "        # normalize the attention weights\n",
    "        # attn_weights   : [bs x n_heads x max_q_len x q_len]\n",
    "        attn_weights = F.softmax(attn_scores, dim=-1)\n",
    "        attn_weights = self.attn_dropout(attn_weights)\n",
    "\n",
    "        # compute the new values given the attention weights\n",
    "        # output: [bs x n_heads x max_q_len x d_v]\n",
    "        output = torch.matmul(attn_weights, v)\n",
    "\n",
    "        if self.res_attention:\n",
    "            return output, attn_weights, attn_scores\n",
    "        else:\n",
    "            return output, attn_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|exporti\n",
    "class _MultiheadAttention(nn.Module):\n",
    "    def __init__(self, d_model, n_heads, d_k=None, d_v=None, res_attention=False, attn_dropout=0., proj_dropout=0., qkv_bias=True):\n",
    "        \"Multi Head Attention Layer\"\n",
    "        \n",
    "        super().__init__()\n",
    "        d_k = d_v = d_model // n_heads\n",
    "\n",
    "        self.n_heads, self.d_k, self.d_v = n_heads, d_k, d_v\n",
    "\n",
    "        self.W_Q = nn.Linear(d_model, d_k * n_heads, bias=qkv_bias)\n",
    "        self.W_K = nn.Linear(d_model, d_k * n_heads, bias=qkv_bias)\n",
    "        self.W_V = nn.Linear(d_model, d_v * n_heads, bias=qkv_bias)\n",
    "\n",
    "        # Scaled Dot-Product Attention (multiple heads)\n",
    "        self.res_attention = res_attention\n",
    "        self.sdp_attn = _ScaledDotProductAttention(\n",
    "            d_model, n_heads, attn_dropout=attn_dropout, res_attention=self.res_attention)\n",
    "\n",
    "        # Poject output\n",
    "        self.to_out = nn.Sequential(\n",
    "            nn.Linear(n_heads * d_v, d_model), nn.Dropout(proj_dropout))\n",
    "\n",
    "    def forward(self, Q:Tensor, K:Optional[Tensor]=None, V:Optional[Tensor]=None, prev:Optional[Tensor]=None):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            Q:       [batch_size (bs) x max_q_len x d_model]\n",
    "            K, V:    [batch_size (bs) x q_len x d_model]\n",
    "            mask:    [q_len x q_len]\n",
    "        \"\"\"\n",
    "\n",
    "        bs = Q.size(0)\n",
    "        if K is None:\n",
    "            K = Q\n",
    "        if V is None:\n",
    "            V = Q\n",
    "\n",
    "        # Linear (+ split in multiple heads)\n",
    "        q_s = self.W_Q(Q).view(bs, -1, self.n_heads, self.d_k).transpose(1, 2) # q_s: [bs x n_heads x max_q_len x d_k]\n",
    "        k_s = self.W_K(K).view(bs, -1, self.n_heads, self.d_k).permute(0, 2, 3, 1) # k_s: [bs x n_heads x d_k x q_len] - transpose(1,2) + transpose(2,3)\n",
    "        v_s = self.W_V(V).view(bs, -1, self.n_heads, self.d_v).transpose(1, 2) # v_s: [bs x n_heads x q_len x d_v]\n",
    "\n",
    "        # Apply Scaled Dot-Product Attention (multiple heads)\n",
    "        if self.res_attention:\n",
    "            output, attn_weights, attn_scores = self.sdp_attn(q_s, k_s, v_s, prev=prev)\n",
    "        else:\n",
    "            output, attn_weights = self.sdp_attn(q_s, k_s, v_s)\n",
    "        # output: [bs x n_heads x q_len x d_v], attn: [bs x n_heads x q_len x q_len], scores: [bs x n_heads x max_q_len x q_len]\n",
    "\n",
    "        # back to the original inputs dimensions\n",
    "        output = output.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * self.d_v)  # output: [bs x q_len x n_heads * d_v]\n",
    "        output = self.to_out(output)\n",
    "\n",
    "        if self.res_attention:\n",
    "            return output, attn_weights, attn_scores\n",
    "        else:\n",
    "            return output, attn_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "class Flatten_Head(nn.Module):\n",
    "    def __init__(self, individual, n_vars, nf, pred_dim):\n",
    "        super().__init__()\n",
    "        \n",
    "        if isinstance(pred_dim, (tuple, list)):\n",
    "            pred_dim = pred_dim[-1]\n",
    "        self.individual = individual\n",
    "        self.n = n_vars if individual else 1\n",
    "        self.nf, self.pred_dim = nf, pred_dim\n",
    "        \n",
    "        if individual:\n",
    "            self.layers = nn.ModuleList()\n",
    "            for i in range(self.n):\n",
    "                self.layers.append(nn.Sequential(nn.Flatten(start_dim=-2), nn.Linear(nf, pred_dim)))\n",
    "        else:\n",
    "            self.layer = nn.Sequential(nn.Flatten(start_dim=-2), nn.Linear(nf, pred_dim))\n",
    "            \n",
    "    def forward(self, x:Tensor): \n",
    "        \"\"\"\n",
    "        Args:\n",
    "            x: [bs x nvars x d_model x n_patch]\n",
    "            output: [bs x nvars x pred_dim]\n",
    "        \"\"\"\n",
    "        if self.individual: \n",
    "            x_out = []\n",
    "            for i, layer in enumerate(self.layers):\n",
    "                x_out.append(layer(x[:, i]))\n",
    "            x = torch.stack(x_out, dim=1) \n",
    "            return x\n",
    "        else: \n",
    "            return self.layer(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|exporti\n",
    "class _TSTiEncoderLayer(nn.Module):\n",
    "    def __init__(self, q_len, d_model, n_heads, d_k=None, d_v=None, d_ff=256, store_attn=False,\n",
    "                 norm='BatchNorm', attn_dropout=0, dropout=0., bias=True, activation=\"gelu\", res_attention=False, pre_norm=False):\n",
    "        super().__init__()\n",
    "        assert not d_model%n_heads, f\"d_model ({d_model}) must be divisible by n_heads ({n_heads})\"\n",
    "        d_k = d_model // n_heads if d_k is None else d_k\n",
    "        d_v = d_model // n_heads if d_v is None else d_v\n",
    "\n",
    "        # Multi-Head attention\n",
    "        self.res_attention = res_attention\n",
    "        self.self_attn = _MultiheadAttention(d_model, n_heads, d_k, d_v, attn_dropout=attn_dropout, \n",
    "                                             proj_dropout=dropout, res_attention=res_attention)\n",
    "\n",
    "        # Add & Norm\n",
    "        self.dropout_attn = nn.Dropout(dropout)\n",
    "        if \"batch\" in norm.lower():\n",
    "            self.norm_attn = nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2))\n",
    "        else:\n",
    "            self.norm_attn = nn.LayerNorm(d_model)\n",
    "\n",
    "        # Position-wise Feed-Forward\n",
    "        self.ff = nn.Sequential(nn.Linear(d_model, d_ff, bias=bias),\n",
    "                                get_act_fn(activation),\n",
    "                                nn.Dropout(dropout),\n",
    "                                nn.Linear(d_ff, d_model, bias=bias))\n",
    "\n",
    "        # Add & Norm\n",
    "        self.dropout_ffn = nn.Dropout(dropout)\n",
    "        if \"batch\" in norm.lower():\n",
    "            self.norm_ffn = nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2))\n",
    "        else:\n",
    "            self.norm_ffn = nn.LayerNorm(d_model)\n",
    "\n",
    "        self.pre_norm = pre_norm\n",
    "        self.store_attn = store_attn\n",
    "\n",
    "\n",
    "    def forward(self, src:Tensor, prev:Optional[Tensor]=None):\n",
    "        \n",
    "        \"\"\"\n",
    "        Args:\n",
    "            src: [bs x q_len x d_model]\n",
    "        \"\"\"\n",
    "\n",
    "        # Multi-Head attention sublayer\n",
    "        if self.pre_norm:\n",
    "            src = self.norm_attn(src)\n",
    "        ## Multi-Head attention\n",
    "        if self.res_attention:\n",
    "            src2, attn, scores = self.self_attn(src, src, src, prev)\n",
    "        else:\n",
    "            src2, attn = self.self_attn(src, src, src)\n",
    "        if self.store_attn:\n",
    "            self.attn = attn\n",
    "        ## Add & Norm\n",
    "        src = src + self.dropout_attn(src2) # Add: residual connection with residual dropout\n",
    "        if not self.pre_norm:\n",
    "            src = self.norm_attn(src)\n",
    "\n",
    "        # Feed-forward sublayer\n",
    "        if self.pre_norm:\n",
    "            src = self.norm_ffn(src)\n",
    "        ## Position-wise Feed-Forward\n",
    "        src2 = self.ff(src)\n",
    "        ## Add & Norm\n",
    "        src = src + self.dropout_ffn(src2) # Add: residual connection with residual dropout\n",
    "        if not self.pre_norm:\n",
    "            src = self.norm_ffn(src)\n",
    "\n",
    "        if self.res_attention:      \n",
    "            return src, scores\n",
    "        else:\n",
    "            return src"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|exporti\n",
    "class _TSTiEncoder(nn.Module):  #i means channel-independent\n",
    "    def __init__(self, c_in, patch_num, patch_len, n_layers=3, d_model=128, n_heads=16, d_k=None, d_v=None, \n",
    "                 d_ff=256, norm='BatchNorm', attn_dropout=0., dropout=0., act=\"gelu\", store_attn=False, \n",
    "                 res_attention=True, pre_norm=False):\n",
    "        \n",
    "        super().__init__()\n",
    "        \n",
    "        self.patch_num = patch_num\n",
    "        self.patch_len = patch_len\n",
    "        \n",
    "        # Input encoding\n",
    "        q_len = patch_num\n",
    "        self.W_P = nn.Linear(patch_len, d_model) # Eq 1: projection of feature vectors onto a d-dim vector space\n",
    "        self.seq_len = q_len\n",
    "\n",
    "        # Positional encoding\n",
    "        W_pos = torch.empty((q_len, d_model))\n",
    "        nn.init.uniform_(W_pos, -0.02, 0.02)\n",
    "        self.W_pos = nn.Parameter(W_pos)\n",
    "\n",
    "        # Residual dropout\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "\n",
    "        # Encoder\n",
    "        self.layers = nn.ModuleList([_TSTiEncoderLayer(q_len, d_model, n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm,\n",
    "                                                      attn_dropout=attn_dropout, dropout=dropout,\n",
    "                                                      activation=act, res_attention=res_attention,\n",
    "                                                      pre_norm=pre_norm, store_attn=store_attn) for i in range(n_layers)])\n",
    "        self.res_attention = res_attention\n",
    "\n",
    "        \n",
    "    def forward(self, x:Tensor):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            x: [bs x nvars x patch_len x patch_num]\n",
    "        \"\"\"\n",
    "        \n",
    "        n_vars = x.shape[1]\n",
    "        # Input encoding\n",
    "        x = x.permute(0,1,3,2)                                                   # x: [bs x nvars x patch_num x patch_len]\n",
    "        x = self.W_P(x)                                                          # x: [bs x nvars x patch_num x d_model]\n",
    "\n",
    "        x = torch.reshape(x, (x.shape[0]*x.shape[1],x.shape[2],x.shape[3]))      # x: [bs * nvars x patch_num x d_model]\n",
    "        x = self.dropout(x + self.W_pos)                                         # x: [bs * nvars x patch_num x d_model]\n",
    "\n",
    "        # Encoder\n",
    "        if self.res_attention:\n",
    "            scores = None\n",
    "            for mod in self.layers: \n",
    "                x, scores = mod(x, prev=scores)\n",
    "        else:\n",
    "            for mod in self.layers: x = mod(x)\n",
    "        x = torch.reshape(x, (-1,n_vars,x.shape[-2],x.shape[-1]))                # x: [bs x nvars x patch_num x d_model]\n",
    "        x = x.permute(0,1,3,2)                                                   # x: [bs x nvars x d_model x patch_num]\n",
    "        \n",
    "        return x "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|exporti\n",
    "class _PatchTST_backbone(nn.Module):\n",
    "    def __init__(self, c_in, seq_len, pred_dim, patch_len, stride, \n",
    "                 n_layers=3, d_model=128, n_heads=16, d_k=None, d_v=None,\n",
    "                 d_ff=256, norm='BatchNorm', attn_dropout=0., dropout=0., \n",
    "                 act=\"gelu\", res_attention=True, pre_norm=False, store_attn=False,\n",
    "                 padding_patch=True, individual=False, \n",
    "                 revin=True, affine=True, subtract_last=False):\n",
    "        \n",
    "        super().__init__()\n",
    "        \n",
    "        # RevIn\n",
    "        self.revin = revin\n",
    "        self.revin_layer = RevIN(c_in, affine=affine, subtract_last=subtract_last)\n",
    "\n",
    "        # # Patching\n",
    "        self.patch_len = patch_len\n",
    "        self.stride = stride\n",
    "        self.padding_patch = padding_patch\n",
    "        patch_num = int((seq_len - patch_len) / stride + 1) + 1\n",
    "        self.patch_num = patch_num\n",
    "        self.padding_patch_layer = nn.ReplicationPad1d((stride, 0)) # original padding at the end\n",
    "        \n",
    "        # Unfold\n",
    "        self.unfold = nn.Unfold(kernel_size=(1, patch_len), stride=stride)\n",
    "        self.patch_len = patch_len\n",
    "\n",
    "        # Backbone \n",
    "        self.backbone = _TSTiEncoder(c_in, patch_num=patch_num, patch_len=patch_len,\n",
    "                                     n_layers=n_layers, d_model=d_model, n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff,\n",
    "                                     attn_dropout=attn_dropout, dropout=dropout, act=act,\n",
    "                                     res_attention=res_attention, pre_norm=pre_norm, store_attn=store_attn)\n",
    "\n",
    "        # Head\n",
    "        self.head_nf = d_model * patch_num\n",
    "        self.n_vars = c_in\n",
    "        self.individual = individual\n",
    "        self.head = Flatten_Head(self.individual, self.n_vars, self.head_nf, pred_dim)\n",
    "        \n",
    "    \n",
    "    def forward(self, z:Tensor): \n",
    "        \"\"\"\n",
    "        Args:\n",
    "            z: [bs x c_in x seq_len]\n",
    "        \"\"\"\n",
    "        \n",
    "        # norm\n",
    "        if self.revin:\n",
    "            z = self.revin_layer(z, torch.tensor(True, dtype=torch.bool))\n",
    "            \n",
    "        # do patching\n",
    "        z = self.padding_patch_layer(z)\n",
    "        b, c, s = z.size()\n",
    "        z = z.reshape(-1, 1, 1, s)\n",
    "        z = self.unfold(z)\n",
    "        z = z.permute(0, 2, 1).reshape(b, c, -1, self.patch_len).permute(0, 1, 3, 2)\n",
    "        \n",
    "        # model\n",
    "        z = self.backbone(z) # z: [bs x nvars x d_model x patch_num]\n",
    "        z = self.head(z) # z: [bs x nvars x pred_dim] \n",
    "        \n",
    "        # denorm\n",
    "        if self.revin:\n",
    "            z = self.revin_layer(z, torch.tensor(False, dtype=torch.bool))\n",
    "        return z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "class PatchTST(nn.Module):\n",
    "    def __init__(self,\n",
    "         c_in,  # number of input channels\n",
    "         c_out, # used for compatibility\n",
    "         seq_len,  # input sequence length\n",
    "         pred_dim=None,  # prediction sequence length\n",
    "         n_layers=2,  # number of encoder layers\n",
    "         n_heads=8,  # number of heads\n",
    "         d_model=512,  # dimension of model\n",
    "         d_ff=2048,  # dimension of fully connected network (fcn)\n",
    "         dropout=0.05,  # dropout applied to all linear layers in the encoder\n",
    "         attn_dropout=0.,  # dropout applied to the attention scores\n",
    "         patch_len=16,  # patch_len\n",
    "         stride=8,  # stride\n",
    "         padding_patch=True,  # flag to indicate if padded is added if necessary\n",
    "         revin=True,  # RevIN\n",
    "         affine=False,  # RevIN affine\n",
    "         individual=False,  # individual head\n",
    "         subtract_last=False,  # subtract_last\n",
    "         decomposition=False,  # apply decomposition\n",
    "         kernel_size=25,  # decomposition kernel size\n",
    "         activation=\"gelu\", # activation function of intermediate layer, relu or gelu.\n",
    "         norm='BatchNorm',  # type of normalization layer used in the encoder\n",
    "         pre_norm=False, # flag to indicate if normalization is applied as the first step in the sublayers\n",
    "         res_attention=True,  # flag to indicate if Residual MultiheadAttention should be used\n",
    "         store_attn=False,  # can be used to visualize attention weights\n",
    "         ):\n",
    "\n",
    "        super().__init__()\n",
    "\n",
    "        # model\n",
    "        if pred_dim is None:\n",
    "            pred_dim = seq_len\n",
    "        \n",
    "        self.decomposition = decomposition\n",
    "        if self.decomposition:\n",
    "            self.decomp_module = SeriesDecomposition(kernel_size)\n",
    "            self.model_trend = _PatchTST_backbone(c_in=c_in, seq_len=seq_len, pred_dim=pred_dim,\n",
    "                                                  patch_len=patch_len, stride=stride, n_layers=n_layers, d_model=d_model,\n",
    "                                                  n_heads=n_heads, d_ff=d_ff, norm=norm, attn_dropout=attn_dropout,\n",
    "                                                  dropout=dropout, act=activation, res_attention=res_attention, pre_norm=pre_norm, \n",
    "                                                  store_attn=store_attn, padding_patch=padding_patch, \n",
    "                                                  individual=individual, revin=revin, affine=affine, subtract_last=subtract_last)\n",
    "            self.model_res = _PatchTST_backbone(c_in=c_in, seq_len=seq_len, pred_dim=pred_dim, \n",
    "                                                patch_len=patch_len, stride=stride, n_layers=n_layers, d_model=d_model,\n",
    "                                                n_heads=n_heads, d_ff=d_ff, norm=norm, attn_dropout=attn_dropout,\n",
    "                                                dropout=dropout, act=activation, res_attention=res_attention, pre_norm=pre_norm, \n",
    "                                                store_attn=store_attn, padding_patch=padding_patch, \n",
    "                                                individual=individual, revin=revin, affine=affine, subtract_last=subtract_last)\n",
    "            self.patch_num = self.model_trend.patch_num\n",
    "        else:\n",
    "            self.model = _PatchTST_backbone(c_in=c_in, seq_len=seq_len, pred_dim=pred_dim, \n",
    "                                            patch_len=patch_len, stride=stride, n_layers=n_layers, d_model=d_model,\n",
    "                                            n_heads=n_heads, d_ff=d_ff, norm=norm, attn_dropout=attn_dropout,\n",
    "                                            dropout=dropout, act=activation, res_attention=res_attention, pre_norm=pre_norm, \n",
    "                                            store_attn=store_attn, padding_patch=padding_patch, \n",
    "                                            individual=individual, revin=revin, affine=affine, subtract_last=subtract_last)\n",
    "            self.patch_num = self.model.patch_num\n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"Args:\n",
    "            x: rank 3 tensor with shape [batch size x features x sequence length]\n",
    "        \"\"\"\n",
    "        if self.decomposition:\n",
    "            res_init, trend_init = self.decomp_module(x)\n",
    "            res = self.model_res(res_init)\n",
    "            trend = self.model_trend(trend_init)\n",
    "            x = res + trend\n",
    "        else:\n",
    "            x = self.model(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model parameters: 418470\n"
     ]
    }
   ],
   "source": [
    "from fastcore.test import test_eq\n",
    "from tsai.models.utils import count_parameters\n",
    "\n",
    "bs = 32\n",
    "c_in = 9  # aka channels, features, variables, dimensions\n",
    "c_out = 1\n",
    "seq_len = 60\n",
    "pred_dim = 20\n",
    "\n",
    "xb = torch.randn(bs, c_in, seq_len)\n",
    "\n",
    "arch_config=dict(\n",
    "        n_layers=3,  # number of encoder layers\n",
    "        n_heads=16,  # number of heads\n",
    "        d_model=128,  # dimension of model\n",
    "        d_ff=256,  # dimension of fully connected network (fcn)\n",
    "        attn_dropout=0.,\n",
    "        dropout=0.2,  # dropout applied to all linear layers in the encoder\n",
    "        patch_len=16,  # patch_len\n",
    "        stride=8,  # stride\n",
    "    )\n",
    "\n",
    "model = PatchTST(c_in, c_out, seq_len, pred_dim, **arch_config)\n",
    "test_eq(model.to(xb.device)(xb).shape, [bs, c_in, pred_dim])\n",
    "print(f'model parameters: {count_parameters(model)}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test conversion to Torchscript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original  : ok\n",
      "tracing   : ok\n",
      "scripting : failed\n"
     ]
    }
   ],
   "source": [
    "import gc\n",
    "import os\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from fastcore.test import test_eq, test_close\n",
    "\n",
    "\n",
    "bs = 1\n",
    "new_bs = 8\n",
    "c_in = 3\n",
    "c_out = 1\n",
    "seq_len = 96\n",
    "pred_dim = 20\n",
    "\n",
    "# module\n",
    "model = PatchTST(c_in, c_out, seq_len, pred_dim)\n",
    "model = model.eval()\n",
    "\n",
    "# input data\n",
    "inp = torch.rand(bs, c_in, seq_len)\n",
    "new_inp = torch.rand(new_bs, c_in, seq_len)\n",
    "\n",
    "# original\n",
    "try:\n",
    "    output = model(inp)\n",
    "    new_output = model(new_inp)\n",
    "    print(f'{\"original\":10}: ok')\n",
    "except:\n",
    "    print(f'{\"original\":10}: failed')\n",
    "\n",
    "# tracing\n",
    "try:\n",
    "    traced_model = torch.jit.trace(model, inp)\n",
    "    file_path = f\"_test_traced_model.pt\"\n",
    "    torch.jit.save(traced_model, file_path)\n",
    "    traced_model = torch.jit.load(file_path)\n",
    "    test_eq(output, traced_model(inp))\n",
    "    test_eq(new_output, traced_model(new_inp))\n",
    "    os.remove(file_path)\n",
    "    del traced_model\n",
    "    gc.collect()\n",
    "    print(f'{\"tracing\":10}: ok')\n",
    "except:\n",
    "    print(f'{\"tracing\":10}: failed')\n",
    "\n",
    "# scripting\n",
    "try:\n",
    "    scripted_model = torch.jit.script(model)\n",
    "    file_path = f\"_test_scripted_model.pt\"\n",
    "    torch.jit.save(scripted_model, file_path)\n",
    "    scripted_model = torch.jit.load(file_path)\n",
    "    test_eq(output, scripted_model(inp))\n",
    "    test_eq(new_output, scripted_model(new_inp))\n",
    "    os.remove(file_path)\n",
    "    del scripted_model\n",
    "    gc.collect()\n",
    "    print(f'{\"scripting\":10}: ok')\n",
    "except:\n",
    "    print(f'{\"scripting\":10}: failed')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test conversion to onnx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "onnx      : ok\n"
     ]
    }
   ],
   "source": [
    "#|onnx\n",
    "try:\n",
    "    import onnx\n",
    "    import onnxruntime as ort\n",
    "    \n",
    "    try:\n",
    "        file_path = \"_model_cpu.onnx\"\n",
    "        torch.onnx.export(model.cpu(),               # model being run\n",
    "                        inp,                       # model input (or a tuple for multiple inputs)\n",
    "                        file_path,                 # where to save the model (can be a file or file-like object)\n",
    "                        input_names = ['input'],   # the model's input names\n",
    "                        output_names = ['output'], # the model's output names\n",
    "                        dynamic_axes={\n",
    "                            'input'  : {0 : 'batch_size'}, \n",
    "                            'output' : {0 : 'batch_size'}} # variable length axes\n",
    "                        )\n",
    "\n",
    "\n",
    "        # Load the model and check it's ok\n",
    "        onnx_model = onnx.load(file_path)\n",
    "        onnx.checker.check_model(onnx_model)\n",
    "        del onnx_model\n",
    "        gc.collect()\n",
    "\n",
    "        # New session\n",
    "        ort_sess = ort.InferenceSession(file_path)\n",
    "        output_onnx = ort_sess.run(None, {'input': inp.numpy()})[0]\n",
    "        test_close(output.detach().numpy(), output_onnx)\n",
    "        new_output_onnx = ort_sess.run(None, {'input': new_inp.numpy()})[0]\n",
    "        test_close(new_output.detach().numpy(), new_output_onnx)\n",
    "        os.remove(file_path)\n",
    "        print(f'{\"onnx\":10}: ok')\n",
    "    except:\n",
    "        print(f'{\"onnx\":10}: failed')\n",
    "\n",
    "except ImportError:\n",
    "    print('onnx and onnxruntime are not installed. Please install them to run this test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": "IPython.notebook.save_checkpoint();",
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       "<IPython.core.display.Javascript object>"
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/nacho/notebooks/tsai/nbs/050b_models.PatchTST.ipynb saved at 2023-02-06 20:14:15\n",
      "Correct notebook to script conversion! 😃\n",
      "Monday 06/02/23 20:14:18 CET\n"
     ]
    },
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "#|eval: false\n",
    "#|hide\n",
    "from tsai.export import get_nb_name; nb_name = get_nb_name(locals())\n",
    "from tsai.imports import create_scripts; create_scripts(nb_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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